Microaneurysms (MA) is one of the earliest clinical sign of diabetic retinopathy (DR), thus automatically detection of MA is a critical step to prevent the development of vision threatening eye diseases such as diabetic retinopathy from being blindness. However, in an actual detection, several factors, including the variation in image lighting, the variability of image clarity, occurrence of other red lesion, extremely low contrast and highly variable image background texture, make the accurate detection of MA difficult. For example, normal MA appears as small red dots in color fundus images, while small dot shaped hemorrhage and vessels conjunctions may also appear as small red dots, making the detection challenging for filter-based and morphological-based method. There are three types of methods for the microvascular detection, such as a detection method based on morphology, a detection method based on wavelet transformation and mathematics transformation, and a detection method based on machine learning. The detection method based on machine learning usually adopts a shallow neural network, which supports vector machines and other classifiers. However, whether the method is based on the machine learning, the aforementioned methods are based on the researcher's priori knowledge and strong unproven assumptions that are usually established only in some cases. In addition, the aforementioned methods need to repeatedly consider how to merge the lesion shape information in the segmentation method. As an inimitable symbol of the MA is very low in the fundus image, the detection requires a very sensitive classifier which required to have stability in the cases of image noise interference and other lesions interference. However, the shallow neural network, support vector machine and other classifier cannot meet its requirements of sensitivity and stability.
After reviewing the prior art, “Automated microaneurysm detection method based on double ring filter in retinal fundus images” is published in “SPIE medical imaging” on page 72601N at 2009 by A. Mizutani, which has asserted a graphic filter of Double-Ring-Filter. The filter is used to calculate an average pixel gray value of two different concentric rings. Since the smaller ring covers most of the microvascular area, it will have a smaller average gray value. Rather, the larger ring covers no or only less of the microvascular lesion area, which has a larger average gray value. An area having a larger average gray value in the two different concentric rings will be served as a candidate area. However, the aforementioned method cannot avoid the noise, the small blood vessel whose diameter is similar to the microaneurysm being classified as the microaneurysm, therefore a candidate point needs to be further processed. “Optimal wavelet transform for the detection of the microaneurysm in retina photographs” is published in “IEEE Transactions on Medical Imaging” from pages 1230 to 1241 at 2008 by Quellec, which has asserted the detection method based on the wavelet transformation. The algorithm mainly uses a local template matcher after the wavelet transformation and finds a position of the microaneurysm lesion using direction decent in the region which is matched with the template after an image is transformed based on the wavelet transformation. The aforementioned method uses a two-dimensional symmetrical Gaussian equation to establish a model for the gray map of the microaneurysm, so as to create the template of the microaneurysm. However, the gray distribution of microaneurysm in this method is only observed by a large number of examples, with the development of shooting technology, the actual distribution may be inconsistent with the hypothesis. “Detection and Classification of Microaneurysms Using DTCWT and Log Gabor Features in Retinal Images” is published in “Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)” from pages 589 to 596 at 2015 by S. Angadi, which has asserted that a binary tree multiple wavelet transformion and a gabor feature are both used as an image feature based on support vector machine (SVM), texture features are extracted from the image, and the SVM is used as the classifier to classify the images. The SVM is a parameter-sensitive classifier, it is necessary to manually adjust the parameters or search for the best parameters in a high-dimensional space to achieve a good result. Such that it is difficult for the usage of the SVM and the aforementioned method is limited. Furthermore, since the SVM itself can only partition linearly separable problems, if the algorithm is acquired to expand or add new features, which will lead to readjust the parameters and even redesign the nuclear equation.